public void CalculateOutput_HasAllInputs_CalculatesAndStoresValue()
{
var neuron = new Neuron();
neuron.AddInputNeuron(new Neuron(14), .5m);
neuron.AddInputNeuron(new Neuron(29), .25m);
neuron.AddInputNeuron(new Neuron(4), -.75m);
Assert.That(neuron.CalculateOutput(), Is.EqualTo(11.25m));
Assert.That(neuron.CalculateOutput(), Is.EqualTo(11.25m));
}
public void CalculateOutput_MultiLayer_CalculatesAndStoresValue()
{
var inputNeuron1 = new Neuron("inputNeuron1", 1);
var inputNeuron2 = new Neuron("inputNeuron2", 2);
var layer1Neuron1 = new Neuron("layer1Neuron1");
var layer1Neuron2 = new Neuron("layer1Neuron2");
var outNeuron1 = new Neuron("outNeuron1");
var outNeuron2 = new Neuron("outNeuron2");
layer1Neuron1.AddInputNeuron(inputNeuron1, .1m);
layer1Neuron1.AddInputNeuron(inputNeuron2, .2m);
layer1Neuron2.AddInputNeuron(inputNeuron1, .3m);
layer1Neuron2.AddInputNeuron(inputNeuron2, .4m);
outNeuron1.AddInputNeuron(layer1Neuron1, .5m);
outNeuron1.AddInputNeuron(layer1Neuron2, .6m);
outNeuron2.AddInputNeuron(layer1Neuron1, .7m);
outNeuron2.AddInputNeuron(layer1Neuron2, .8m);
Assert.That(outNeuron1.CalculateOutput(), Is.EqualTo(0.91m));
Assert.That(outNeuron2.CalculateOutput(), Is.EqualTo(1.23m));
}
public void CalculateOutput_CorrectValues()
{
var n = new Neuron(2);
n.weights = new List <double> {
1.0, 2.0
};
n.bias = -1.0;
Assert.AreEqual(0.9525741268224331, n.CalculateOutput(new List <double> {
2.0, 1.0
}));
Assert.AreEqual(0.5, n.CalculateOutput(new List <double> {
3.0, -1.0
}));
}
public void CalculateOutput_MockingFunctions_OutputReturned()
{
var activationFunction = new Mock <IActivationFunction>();
activationFunction.Setup(x => x.CalculateOutput(It.IsAny <double>())).Returns(111);
var inputFunction = new Mock <IInputFunction>();
inputFunction.Setup(x => x.CalculateInput(It.IsAny <List <ISynapse> >())).Returns(23);
var neuron = new Neuron(activationFunction.Object, inputFunction.Object);
Assert.AreEqual(111, neuron.CalculateOutput());
}
public void CalculateOutput_ShouldWork()
{
int[] neuronAmounts = new int[] { 4, 3, 5 };
NetworkBuilder networkBuilder = new();
Network network = networkBuilder.AddLayers(neuronAmounts).Build();
network.Layers[0].Neurons.ForEach(n => n.Weights = new List <double>(new double[] { .1, .2, .3, .4 }));
network.Layers[1].Neurons.ForEach(n => n.Weights = new List <double>(new double[] { .4, .5, .6, .7, .8 }));
double expectedOutput = 12.5;
double actualOutput = Neuron.CalculateOutput(new double[] { 5, 6, 2, 3 }, new double[] { .9, .8, .7, .6 });
Assert.Equal(expectedOutput, actualOutput);
}
